# Deep learning for class-generic object detection

Source: [Deep learning for class-generic object detection](<https://www.sota2.com/research/paper/68fa61291e209131ce9f2da0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:53:34.667Z

Authors: Brody Huval, Adam Coates, Andrew Ng

Published: 2013-12-24T20:38:18.000Z

## Abstract

We investigate the use of deep neural networks for the novel task of class generic object detection. We show that neural networks originally designed for image recognition can be trained to detect objects within images, regardless of their class, including objects for which no bounding box labels have been provided. In addition, we show that bounding box labels yield a 1% performance increase on the ImageNet recognition challenge.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1312.6885>)

[PDF](<https://arxiv.org/pdf/1312.6885.pdf>)